arXiv AI By Dekun Yang

Where Grokking Happens: Distributed Utility and Fourier Recoding Without a Module Switch

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The paper investigates where the transition from memorization to generalization occurs within a Transformer. Using Transition Games—activation games with paired non-generalizing controls—it finds that distributed utility gain is driven by a block‑0 attention bias, with degree‑two modes explaining 67–92% of the contrast across replacement games. A detailed path study shows that block‑1 MLP mediates more of this effect than other downstream paths, contradicting the common "MLP memorizes, attention generalizes" hypothesis and suggesting that grokking is a spectral recoding of an existing distributed circuit rather than a module switch.

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arXiv Machine Learning
Jul 10

What to Keep, What to Forget: A Rate--Distortion View of Memory Compaction in LLMs and Agents

arXiv:2607. 08032v1 Announce Type: new Abstract: Large language models, and the agents built on them, spend an ever-growing share of their compute and memory on remembering: caching attention keys and values, carrying long prompts, maintaining recurrent state, and storing what happened in previous turns and sessions.

By Ashwin Gerard Colaco, Nada Lahjouji